4 citations · 8 across the 8 of their papers we have counts for
8 papers
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning
Yingjie Fei, Ruitu Xu
We study risk-sensitive multi-agent reinforcement learning under general-sum Markov games, where agents optimize the entropic risk measure of rewards with possibly diverse risk pre…
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning
Ruitu Xu, Yifei Min, Tianhao Wang +3
We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market. Each household earns income and engages in consumpti…
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
Yifei Min, Tianhao Wang, Ruitu Xu +3
We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market. At each step, the agents are presented with a dynamical context,…
Cascaded Gaps: Towards Gap-Dependent Regret for Risk-Sensitive Reinforcement Learning
Yingjie Fei, Ruitu Xu
In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement learning based on the entropic risk measure. We propose a novel definition of sub-optimalit…
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights
Ganlin Song, Ruitu Xu, John Lafferty
Stochastic gradient descent with backpropagation is the workhorse of artificial neural networks. It has long been recognized that backpropagation fails to be a biologically plausib…
Meta Learning in the Continuous Time Limit
Ruitu Xu, Lin Chen, Amin Karbasi
In this paper, we establish the ordinary differential equation (ODE) that underlies the training dynamics of Model-Agnostic Meta-Learning (MAML). Our continuous-time limit view of…
On the Landscape of Synchronization Networks: A Perspective from Nonconvex Optimization
Shuyang Ling, Ruitu Xu, Afonso S. Bandeira
Studying the landscape of nonconvex cost function is key towards a better understanding of optimization algorithms widely used in signal processing, statistics, and machine learnin…
Learning the Kernel for Classification and Regression
Chen Li, Luca Venturi, Ruitu Xu
We investigate a series of learning kernel problems with polynomial combinations of base kernels, which will help us solve regression and classification problems. We also perform s…